Topographical models of geographical areas may be used for many applications. For example, topographical models may be used in flight simulators and other planning missions. Furthermore, topographical models of man-made structures, for example, cities, may be extremely helpful in applications, such as, cellular antenna placement, urban planning, disaster preparedness and analysis, and mapping.
Various types of topographical models are presently being used. One common topographical model is the digital elevation model (DEM). A DEM is a sampled matrix representation of a geographical area, which may be generated in an automated fashion by a computer. In a DEM, coordinate points are made to correspond with a height value. DEMs are typically used for modeling terrain where the transitions between different elevations, for example, valleys, mountains, are generally smooth from one to another. That is, a basic DEM typically models terrain as a plurality of curved surfaces and any discontinuities therebetween are thus “smoothed” over. Another common topographical model is the digital surface model (DSM). The DSM is similar to the DEM but may be considered as further including details regarding buildings, vegetation, and roads, in addition to information relating to terrain.
One particularly advantageous 3D site modeling product is RealSite®, as available from the Harris Corporation of Melbourne, Fla. (Harris Corp.), the assignee of the present application. RealSite® may be used to register overlapping images of a geographical area of interest and extract high resolution DEMs or DSMs using stereo and nadir view techniques. RealSite® provides a semi-automated process for making three-dimensional (3D) topographical models of geographical areas, including cities, that have accurate textures and structure boundaries. Moreover, RealSite® models are geospatially accurate. That is, the location of any given point within the model corresponds to an actual location in the geographical area with very high accuracy. The data used to generate RealSite® models may include aerial and satellite photography, electro-optical, infrared, and light detection and ranging (LIDAR), for example.
Another similar system available from the Harris Corp. is LiteSite®. LiteSite® models provide automatic extraction of ground, foliage, and urban digital elevation models (DEMs) from LIDAR and synthetic aperture radar (SAR)/interfermetric SAR (IFSAR) imagery. LiteSite® can be used to produce affordable, geospatially accurate, high-resolution 3-D models of buildings and terrain.
U.S. Pat. No. 6,654,690 to Rahmes et al., which is also assigned to the present assignee and is hereby incorporated herein in its entirety by reference, discloses an automated method for making a topographical model of an area including terrain and buildings thereon based upon randomly spaced data of elevation versus position. The method includes processing the randomly spaced data to generate gridded data of elevation versus position conforming to a predetermined position grid, processing the gridded data to distinguish building data from terrain data, and performing polygon extraction for the building data to make the topographical model of the area including terrain and buildings thereon.
In some applications, it may be desirable to separate the building and vegetation data in DSMs. Indeed, the aforementioned LiteSite® system may separate the building and vegetation data in DSMs generated from LIDAR data. This functionality is typically partially automated using computer processes, which is desirable given the significant size of most DSMs. A potential drawback to this approach is the use of LIDAR DSMs since such DSMs are expensive and time consuming to collect since a LIDAR enabled mobile platform is tasked to cover the geographical areas.
Nonetheless, such automated functionality may not be available when the DSMs are generated stereographically, i.e. being generated using overlapping images of a geographical area of interest. In these stereographic DSMs, a user typically reviews the DSM manually and separates the building and vegetation data by annotating the DSM. This approach may be time consuming, labor intensive, and expensive.